Analyzing Semantic Segmentation Using Hybrid Human-Machine CRFs Roozbeh
Analyzing Semantic Segmentation Using Hybrid Human-Machine CRFs
Roozbeh Mottaghi1, Sanja Fidler2, Jian Yao2, Raquel Urtasun2, Devi Parikh3
holistic scene understanding models for
We compute CRF potentials based on:
- Responses of human subjects
- Ground Truth
- Removing components
Machine Accuracy: 77.4
Holistic CRF Model
Which scene is depicted?
Which classes are present?
No restrictions on the form
of the potentials
Class Unary: Likelihood of presence of a certain class
Machine: Frequency in training data
Humans: Given a pair of categories, we asked Which category is
Machine: Co-occurrence matrix from training data
Humans: We asked Which scenario is more likely to occur in an
image? Observing (cow and grass) or (cow and airplane)?
J. Yao, S. Fidler and R. Urtasun,
Describing the Scene as a Whole: Joint
Object Detection, Scene Classification and
Semantic Segmentation, CVPR 2012.
Human & Machine Potentials
We used Amazon Mechanical Turk to produce human potentials.
Machine: DPM (Felzenszwalb et al.)
Humans: Ground truth which is provided by humans.
We tried several hypotheses including incorporating scale, handling
overfitting, predicting human potentials from machine potentials,
etc., but none of them explained the boost.
M SS H SS
Human and machine errors become similar when we reduce the
window size for TextonBoost:
Machine: Per component average mask of examples. (78.2%)
Humans: We asked them to draw object contour along the
boundary of superpixels (80.2%)
Machine: 200x200 windows
10 subjects participated in our study for each task (total of 500
In total, we had ~300K tasks.
We used MSRC dataset for our experiments.
Humans and machines make 84complementary mistakes:
Breaking the connection
between layers causes a
significant decrease in accuracy 74
when we have human at one
level and machine at another.
M S (M+H) S(M+H) S
The CRF model performs better with the less accurate human segment
potential. Goal of subsequent analysis is to explain the boost.
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